A pre-order walk of bullet tree on slides is actually a natural choice, since speakers of presentations often follow such an order to develop their talks, i.e., they discuss a parent bullet first and then each of its children in sequence.

although some remedies may be taken (Zhu et al., 2010), sequentializing the hierarchies before alignment, in principle, enforces a full linearity/monotonicity between transcripts and slide trees, which violates some basic properties of the problem that we will discuss.


Second, the better performance of HieCut and SeqCut shows that HieCut further benefits from avoiding sequentializing the bullet trees.

this two aspects of benefit do not come independently, since the former (performance of an alignment objective) can significantly affect the latter (whether a model can benefit from avoiding sequentializing bullets).


Obviously, such a test would take years to perform, and would need to be done again, each time the resources for a language are updated.

a statistical machine translation (SMT) system can attempt this acquisition automatically, and its mistakes highlight any shortcomings in the documentation while there is still time to collect more.


This is painstaking work, and transcription is usually a slow process given the issues with orthography just identified.

such transcriptions are an essential step to the creation of other language resources such as lexicons and grammars.


The algorithm as defined is only applicable to aligning two sentences at a time.

it has a simple extension to allow alignments of N transcriptions simultaneously.


Equation (52) is only correct if word boundaries are treated as states in the HMM, which they are not; state sequence lengths are pre-drawn from a gamma distribution.

the assumption becomes more accurate as the mean word length increases, and the average source block length decreases.


Note that this method selects sentences from Web search results using the keyword queries determined according to TF-IDF.

our main goal is to efficiently identify questions covering a wide range of topics while matching a certain style, often represented by colloquial textual fragments and therefore consisting of frequent words.


The peak performance was obtained when the corpus size had 80 million words consistently in both test data.

the best performance was worse than when we trained on www (16.35% in g1 and 15.28% in g2).


If these are used as training corpus, they may be harmful.

as seeds they only derive trigram probabilities for sentence selection, the selected sentences being natural, real life sentences from the Web.


They present a method to calculate the minimum number of borrowings required to admit that tree.

the method does not actually construct a tree from the data, and it may be computationally intractable when the number of borrowings is large.


Then s and t are not present in any of the same languages, but there are two languages l i , l j ∈ L such that l i has character s but not t, and language l j has character t but not s. If s and t are only present within the language grouping, they are not informative when language family grouping is used.

if both s and t are present at an internal node ancestral to language grouping L, then this will make the data closer to admitting a CDP by decreasing the number of borrowings that we need to posit.


LangID as a computational task is usually attributed to Gold (1967), who sought to investigate language learnability from a language theory perspective.

its current form is much more recognizable in the work of Cavnar and Trenkle (1994), where the authors classified documents according to rank order statistics over byte n-grams between a document and a global language profile.


TextCat (CT ) selects features per language by term frequency, whereas langid.py uses LD bin (the focus of this work).

the learning algorithm used by TextCat is a nearest-prototype method using the token rank difference metric of Cavnar and Trenkle (1994), whereas langid.py uses multinomial naive Bayes.


For name disambiguation in entity linking, there has been much previous work which demonstrates modeling context is an important part of measuring document similarity.

the traditional approach for entity linking treats the context as a bag of words, n-grams, noun phrases or/and co-occurring named entities, and measures context similarity by the comparison of the weighted literal term vectors (Varma et al., 2009;Li et al., 2009;Zhang et al., 2010;Zheng et al., 2010;Dredze et al., 2010).


Each article in Wikipedia is assigned several categories by the contributors as requested.

from our observation some categories in Wikipedia may not be suitable to model the topics of a document.


This should be because isa_all includes more categories than isa_class and isa_instance, and thus can capture more semantic information.

although All and Alladmin include even more categories, they introduce many categories which are unsuitable to model the topics of a news article or blog text, such as the two categories mentioned in Section 3.3, "people by status" which is not in an is-a relation and "Wikipedia editing guidelines" which is used for encyclopedia management.


(2010), the system uses pseudorelevance feedback to expand queries.

the two systems do not take account relations in a query.


Next, other nodes (web pages) of their network are activated.

the above Constrained-SA (CSA) models do not use relations in a given query to constrain spreading.


(2005), the authors use the relations in a query to expand the query.

the work only exploits spatial relations (e.g.


Theoretically, topic detection from microblog text is similar to that from news articles.

the microblog text is rather different 1 http://projects.ldc.upenn.edu/TDT/ 2 http://techcrunch.com/2011/04/06/twitter-q1-stats/ from news articles.


The thread tree has three sub-trees, namely, there are three subtopics within thread .

seen from Figure 4, the right sub-tree is obviously not relevant to the dominating topic.


The notable contribution lies in that the serious sparse data problem in microblog processing is alleviated to great extent.


The unigram language model captures the familiarity of individual words.

we expect the perplexity computed using higher order models to distinguish between common word transitions in the domain, and those that are unexpected and evoke surprise.


We have drawn the conclusion in (Xia et al., 2011) that Sum rule is a low-cost yet effective approach for sentiment classification.

this conclusion may not hold in the cross-domain tasks.


This makes our approach suitable for document filtering purposes.

classification of out-ofdomain data seems difficult because of the covariate shift (Shimodaira, 2000) in feature distribution between domains.


Similar problems refer to the analysis of speaker intentions in conversation (Kadoya et al., 2005) and to the area of textual entailment (Michael, 2009), which is about the information implied by text.

lFA is about the question why a text was written and, thus, refers to the authorial intention behind a text.


Additionally, Boese and Howe (2005) recognized that, in the web, genres may evolve over time to other genres.

language functions still represent one important aspect of genres.


Adding WS even led to a decrease of one percentage point.

this does not mean that the writing style features failed, as we see later on, but seems to be only noise from the optimization process of the SVM.


This indicates that language functions relate to the writing style of a text.

the correlation with sentiment appeared to be low.


According to (Ehling et al., 2007), for each source sentence with N different translations, we could select the final translation based on the following Minimum Bayes Risk principal: e = arg min e { e (P r(e |f ) • (1 − BLEU (e , e)))} (6) Here P r(e |f ) denotes the posterior probability for translation e and BLEU (e , e) represents the sentence-level BLEU score for e using e as reference.

since the translation hypotheses are generated under different groups of weights, the corresponding posterior probability is no longer comparable.


It seems that we need at least 64 particles for a stable tuning in our setting.

when MERT-PSO employed sufficiently many particles, it outperformed Moses-MERT and provided stable convergence even though MERT-PSO is a stochastic optimization method.


Although our new method does produce a significant gain over the best single system, it does not perform as well as our confusion network decoding on any condition that we tried.

this new procedure was not designed to replace our existing method, but rather to complement it.


1 Confusion network based methods align the various input hypotheses against one another to form the confusion network, and then generate the most likely path through this network to produce a combined 1-best (Bangalore et al., 2001).

as mentioned previously, we already have state-ofthe-art confusion network system in place, which is based on (Rosti et al., 2007;Rosti et al., 2009).


When this feature was used by Snover, it combined the standard language model with a test-sentence-specific language model that was trained on several hundred documents.

in the case of system combination, we are adapting towards a much smaller amount of data, so P (q j |q j−1 , ..., q j−n+1 ) will not be well estimated.


Because we use a large number of discriminative features in our baseline MT system, there is a moderate-to-significant over-fitting effect when optimizing on any new set.

in the past we have found that even a large amount of overfitting (e.g., 3-4 BLEU points) on the tuning set does not have a negative affect on the test set results.


( 3)pens ACC pick up feel TOP become not did "(I) did not get into start writing" Because of our strict condition, the size and variation of the extracted labeled data are limited.

this method gave us longer and more natural reliable labeled data.


As shown in § 3, in Iwanami, there are 1,450 original example sentences, such as in a sentence (2), for the target words.

we could use only 362 example sentences to extract labeled instances, such as in a sentence (3).


Step-1 provided superior performance (80.2 %) to the state-of-the-art result (76.4 %), and the high effectiveness of this method is proved.

it may be difficult to achieve any further improvement because the extracted data may have an unnatural sense distribution and limited variations.


The widely used knowledge resource in such methods is WordNet (Fellbaum, 1998).

wordNet-based wSD methods usually achieved lower performance compared to supervised methods, mainly due the fact that the lexical and semantic knowledge contained in wordNet is not sufficient for wSD.


Therefore, if extending WordNet with the large amounts of semantic relations contained in ConceptNet, it is desirable to improve the performances of WordNet-based WSD methods.

conceptNet cannot be directly used for WSD purpose due to the existence of polysemy and synonymy of the concepts in it.


It is obvious that the concepts related to airplane should have the same relation with plane.


Therefore some recent work (Mihalcea, 2007;Ponzetto&Navigli, 2010) exploits Wikipedia, a large collaborative Web encyclopedia, to extract the knowledge for WSD.

the type of semantic relations extracted from Wikipedia is uncertain.


Ideally, the NGD score of any term in the WSP of the correct sense is lower than that in the WSP of incorrect sense, thus we can simply use the arithmetic mean of the scores to evaluate the relatedness of WSP and the assertion.

it is inevitable that there are some noisy terms in WSPs, which will dramatically decrease the performance of disambiguating ConceptNet.


There is no problem for the occurrence of handball in WSP( 1 n handball ).

it is because the computation of NGD score does not consider different senses of the same term in different occurrences, different handballs in different WSPs actually have the same NGD scores though they correspond to different senses.


On one hand, even a simple knowledge-based WSD algorithm using the enriched WordNet can perform as well as the highest-performing supervised ones.

more sophisticated approaches (Agirre&Soroa, 2009; Navigli&Lapata, 2010) may achieve even higher performance by using such enriched WordNet.


This method does not require a parallel corpus.

it requires sense marked corpus for one of the two languages.


This algorithm when tested on 60 polysemous words (using English as L 1 and Japanese as L 2 ) delivered high accuracies (coverage=88.5% and precision=77.7%).

when used in an all-words scenario on our dataset, this algorithm performed poorly (see section 6).


The probability of the dif-  ferent senses of maan can thus be estimated based on the counts of {gardan, galaa} and {aadar, iz-zat}.

since the words {gardan, galaa} and {aadar, izzat} may themselves be ambiguous, their raw counts cannot be used directly for estimating the sense distributions of maan.


On an average the performance of PPR on nouns is better than our algorithm by 3%.

in 2 out of the 4 language-domain pairs our algorithm does better on nouns than PPR (by 6% in HINDI-HEALTH and 2% in MARATHI-HEALTH -see Table 8).


In this setting, a constituent word is typically represented by a feature vector conflating all the senses of that word.

not all the senses of a constituent word are relevant when composing the semantics of the compound.


When trained on the whole ACE 2005 corpus (in a supervised training scenario) this is appropriate behavior: we don't want to report an event in testing if we haven't seen the trigger before.

5 for active learning, the inability to differentiate among potential new triggers and local structures is critical.


If it is the event tagger, this sample is informative for the event tagger and adding this sample will improve the performance; if it is the sentence classifier, it is not guaranteed that this sample is informative for the event tagger.

since the updated sentence classifier will serve to select subsequent queries, samples informative for the sentence classifier should accelerate subsequent active learning.


For example, if we have pattern A which is very similar to some eventbearing patterns in the training data, and pattern B which is quite different from any pattern in the training data, the event tagger will treat them the same.

the sentence classifier provides more graded matching, and gives the sentence containing pattern A higher score because they share a lot of words.


indicated by a common noun or noun phrase, or represented by a pronoun.

wikipedia instances mainly refer to NEs, e.g.


The objective of the word alignment task is to identify translational relationships among the words in a bi-text, and to produce a bipartite graph with a set of edges between words with translational relationships (Figure 2(a)).

the results of automatic word alignment may include incorrect alignments because of technical difficulties.


The baseline model using only languageindependent heuristics achieves poor performance, especially in recall.

our proposed projection-based model outperforms   Table 3: Distribution of the errors the baseline model, due to largely increased recall.


The major problem tackled in such tasks is the handling of unknown words and domain-specific manners of expression.

parsing imperatives and questions involves a significantly different problem; even when all words in a sentence are known, the sentence has a very different structure from declarative sentences.


For Brown overall, compared with the WSJ, the accuracy did not decrease much.

for imperatives and questions, the POS tagger accuracy decreased significantly.


In Figure 3, the parser accuracy for QuestionBank, for which we could use much more training data than for Brown questions, approaches or even exceeds that of the WSJ parser for WSJ.

as there is no more training data for Brown imperatives and questions, we need to either prepare more training data or explore approaches that enable the parsers to be adapted with small amounts of training data.


The adapted parser could handle this dubious construction and assigned ROOT to the main verbs as the corpus required.

10 we also observed some un- 10 We may have to correct the corpus.


For example, since the ni (dative) case of 'yaku (3)' often appears, we assume this slot is just omitted as a zero pronoun even if there is no overt argument.

since the ni (dative) case of 'yaku (1)' rarely appears, we assume there is no case slot if there is no overt argument.


are used to create an abstraction of words unknown to the ERG and then binary classifiers are employed to learn lexical entries for those words.

learning is done based on incomplete information obtained by the various resources used.


For all techniques, the experiments show that their application leads to an increase in coverage compared to the baseline, that is the standard GG setup.

the supertagging methods achieved this at the price of having lower accuracy than the baseline.


Then, the tagger is used to predict lexical types for unknown words in a random sample of 1000 sentences extracted from the FR corpus.

since no annotation is available for the test corpus, the tagger is run with the same features as in  but without the POS tags of the context words.


Accordingly, we attach the values of some of the type features defined in the relevant lexical entries to the type definitions of those entries.

for reasons of data sparseness, we choose to exclude some of the features which Cholakov et al.


overall nouns adj verbs total 2954 1196 651 694 accuracy(%) 96.45 91.09 100 99.54 Table 4: Paradigm generation results In the paradigms generated for verbs there were three mistakes.

the generated verb stems were all correct.


The application of methods, where a tagger was used to predict lexical descriptions for words unknown to the GG grammar of German, led to an increase in parsing coverage on a German newspaper corpus.

accuracy was below the baseline, that is the accuracy of the standard GG setup.


Parsing is one of the fundamental building blocks of natural language processing, with applications ranging from machine translation (Yamada and Knight, 2001) to information extraction (Miyao et al., 2009).

while statistical parsers achieve higher and higher accuracies on in-domain text, the creation of data to train these parsers is labor-intensive, which becomes a bottleneck for smaller languages.


Many existing dependency corpora use phrases as the unit of annotation, and these resources are a valuable potential source of data for mining word dependencies.

phrase dependencies alone do not provide enough information for an automatic conversion to word dependencies.


Huang and Mi (2010) has shown that forest expansion can be done in decoders using beam search (Koehn, 2004).

to the best of our knowledge, no index-based search structures for incrementally finding trees have been proposed to date.


The cache usage will depend on the order in which the forests are searched and also on the content of the forests.

the forests are searched in the same order across all indexes, therefore, their relative cache performances can be meaningfully compared.


Note that all these works are based on the word sequential alignment models.

for distant language pairs such as English-Japanese or Chinese-Japanese, the word sequential model is quite inadequate (about 20 to 30 % AER), and therefore it is important to improve the alignment accuracy itself.


Each application of an operator generates one new sample, and of course we could use all the generated samples.

successive samples are almost the same, except for one local part.


EXPAND-1 does not have any restrictions on its operation.

for EXPAND-2, if the root node has more than one child node inside the subtree, it cannot exclude the root node, because the subtree will be divided into two subtrees by the exclusion, and it is impossible to return to the previous state.


Therefore given a specific test set, the unknown terms covered by our method are just a subset of CB's method.

on such a subset, our method gains significant improvement in translation quality over CB's approach.


For most recent research efforts, English is the pivot language of choice due to the richness of available language resources.

recent research on pivot translation has shown that the usage of non-English pivot languages can improve translation quality for certain language pairs (Paul et al., 2009;Leusch et al., 2010).


Language family and language perplexity seems to have the least impact on translation performance.

when applying linear regression on language subsets (only Euro- For pivot translations between European languages, sentence length, reordering and vocabulary are more predictive than the translation model entropy factor.


We can obtain features from word chunks, such as the first word of a word chunk and the last word of a word chunk, which cannot be obtained in word-sequence-based recognition methods.

each word chunk may include a part of an NE or multiple NEs.


In addition, our method can use features extracted from word chunks that cannot be obtained in word-based NE recognitions.


Ideally, all the possible word chunks of each input should be considered for this algorithm.

the training of this algorithm requires a great deal of memory.


The number of connections is up to K 2 , where K is the number of types of class labels.


This is because SPJR and NECC require training for the SR-based NE chunker and base NE recognition.


In such a corpus, a high frequency string tends to have more accessors which will bring noise to the AV criterion.

noise may be filtered out by a threshold in Eq.


Instance-based evaluation measures the EL performance at a fine-grained IE resolution, which can support the development of advanced IE tasks.

to the first metric, the PRF scores are calculated based on the sums of TP, TN, FP and FN for all instances in the test dataset; we further consider whether the boundary matches that of the linked KB entry's mention.


This shows that the saliency property is effective in instance-based evaluation.

mLN SAL performs slightly worse than mLN LINK in the article-wide evaluation, the reason for which is explained in Section 5.1.


According to our analysis, S.1 improves the recall in the instance-based evaluation.

for article-wide, S.1 slightly reduces the recall.


Word lists are now available for most of the world's languages (Wichmann et al., 2011).

only a fraction of such lists contain cognate information.


However, these methods cannot handle terms that are not Wikipedia-article titles, and thus their coverage is limited.

distributional similarity be-tween terms has been used in extending an existing taxonomy like WordNet (Snow et al., 2006;Yamada et al., 2009).


(2009) linked a target term to its hypernym in a given taxonomy by using distributional similarity between the target term and terms in the taxonomy.

it is often the case that we cannot obtain reliable distributional similarity of a term and we cannot acquire hypernyms of a term co-occurring with lexico-syntactic patterns, especially when the term is infrequent in a corpus.


According to this observation, the top-n synsets among the synsets that contain hyper are generated.

if the hyponymy relation is wrong (like musician as a hypernym of acoustic guitars), this heuristics will have a negative effect on the performance of synset identification.


In WSD, supervised methods are by far the most successful but large amounts of data are required for each word type to be disambiguated (Navigli, 2009).

unlike in WSD, we can expect to find some linguistic commonality between the idiomatic senses of distinct MWE-types.


The idiom token or type features alone did not stand up well in comparison.

we note that combining our type features with the complete idiom token features provided a disproportionate boost to a classification accuracy of almost four percentage points above the baseline.


Our type features and new idiom features, working in concert with the idiom features of Hashimoto and Kawahara (2009), substantially increase crosstype classification performance over the baseline.

their effect is wholly subsumed by the inclusion of WSD features.


Thus, in this paper, we try to identify to which preceding answer and in what relation an answer is related.

some answers are irrelevant to a question and these answers might be unnecessary for an overview of a thread.


after the using NER, which will be helpful to assign this question to correct class.

if we do not tag "W.C. Fields" as a person's name, it will be difficult to correctly identify the question's class.


(2008) calculated user-to-question similarity and questionto-question similarity by employing the PLSA model, then ranked candidate questions by combining the two similarity scores.

it is dif-ficult to model users in K2Q, so this algorithm is not suitable for our purpose.


Since the 3gram meaning of advocacy never occurs in the training set, LM does not rank the target question as the top 1 best candidate.

the 3-gram meaning of WordCluster occurs frequently in the training set, so the UII model ranks the target question as the top 1 best candidate.


In the training set, the phrases how many occurs more frequently than the phrase what are the different, so LM does not rank the target question as its number 1 candidate.

the corresponding user intent has high probability to generate the slot different, so the target question is ranked as the best candidate by the UII model.


As a result, Twitter or social network services (SNS) such as mixi 3 played an important role for propagating safety information among people.

with SNSs flooded with information about the disaster, it was difficult to ensure that people would be properly connected with the information they were seeking.


Good performance was obtained for the O and M that comparatively have a larger number of instances.

the recalls for the labels having few instances were lower.


The manually corrected NE information was matched with GPF data, and we were able to update the personal information of more than a hundred individuals that were confirmed to be alive in tweets.

many tokens extracted from tweets had problems, including: • Incomplete LOCATION information: The tokens lacked a couple of information such as city, or neighborhood.


On a more abstract level, we were able to show that NLP has the potential to make a contribution in a disasterresponse situation, which we hope will provide an impetus for similar future projects.

a number of challenges remain, and we conclude by summarizing the major lessons learned in the project.


There is a large increase in accuracy between using a 3-gram versus a 5-gram model.

the jump from 5-gram to 7-gram is much smaller, and in some cases it even decreases performance.


It further finds the sentiments of these attributes.

our work finds actual values for the attributes and not merely sentiments expressed by users.


Among machine learning approaches CRF (Peng and McCallum, 2006) (Stoyanov and Cardie, 2008) has been effective if we have training data and want to extract template attributes and values.

we do not always have a predefined list of explicit attributes and values which have to be extracted and populated.


The output of these rules are used to populate Data Warehouses and Product Information Management (PIM) systems.

these rules are applied only to short product descriptions which do not lead to a complete view of the product.


We make use of the supervision provided by already existing rulesets frequently used for standardization to get a handle of such values.

as the reviews are verbose and noisy, these values too contain a lot of noise.


Out of the candidates "1369 bucks " and "$ 685" for the price attribute, "1369 bucks" is chosen as it contains more known attribute values (Canon, 2.8D, lens) in its context in review 1.

as "$ 685" has many competing attribute values (which do not match the known values) in its context (Nikon, AF, f/3.5-5.6G), it is rejected.


For instance, if the "Product:Camera" or "Brand:Nikon" matches we still cannot be very sure because the author can be comparing two different cameras or two Nikon products.

if a value mentioning weight or lens of the camera matches, we can be more certain as it is unlikely to have two cameras with exactly the same weight or the same lens.


The problem could be further compounded if the author swings back and forth comparing two products leading us to the deep waters of Pronoun Resolution and Attribute Coreference Resolution.

we easily find a way around them with the assumption that the switch will not happen too frequently in most reviews.


Thus, the internal nodes formed during the hierarchical clustering process shares words with their children.

in the hierarchical topic model the internal nodes are not summaries of their children.


Q&A forum acts not only as a medium for knowledge sharing , but also as a place in which one can seek advice, and satisfy others' curiosity about a countless number of things (Adamic et al., 2008).

because of the ever-increasing growth of it, and the fact that users are not always experts in the areas they post threads on, duplicate content becomes a serious issue.


This approximation can improve the efficiency of the term-base algorithm, based on which the term-based method can process large collections.

the calculated similarities may not reflect the real similarities with this approximation method.


Support Vector Machines, Maximum Entropy, AdaBoost, and so on) can be used to solve the problem with the calculated similarities and extracted information as feature sets.

for simplification, in this paper we use linear combination of different parts' similarities and a predefined threshold τ to do that.


come close to that of our SVM regression model when we only compare the top 3 or 4 candidates.

the performance gap becomes larger as K grows.


Anchor/coreference-based method cannot acquire the argument "taichou(condition)-wo" that presents in one node (which means this argument is shared by no events).

our proposed method can acquire such an argument.


The main purpose of information retrieval (IR) is to provide the user with documents that are relevant to his/her information needs.

it is difficult to achieve this by one-off retrieval, since user queries are typically short and often ambiguous (Jansen et al., 2000).


As described above, many methods have been proposed for RF.

most of the previous methods use only the surface information in texts.


In past KBP competitions, many participants (Li et al., 2009;Byrne and Dunnion, 2010;Chen et al., 2010) exploited a QA system to fill slots by constructing queries based on target entities and slot types.

their query templates contain only a few additional query terms other than the target entity name, which are mostly obtained manually.


Of the total 67 person entities in our test data, the IE-pipeline is not able to extract any employment information for 12 of them.

using the primitive QA-pipeline, we are able to recover 4 of them while introducing 6 new errors.


COGSUM has previously worked without aid from any outside source making it highly portable and more or less language independent.


However, their task was limited to retrieving the argument heads.

we integrate discourse segmentation in the parsing pipeline because we believe that spans are necessary when using the discourse arguments as input to applications such as opinion mining, where attributions need to be explicitly marked.


Attributions occur in 34% of all explicit relations in the PDTB, and represent one of the major challenges in identifying exact argument spans, especially for Arg2.

given the fact that Arg2 is syntactically bound to the connective, its identification is generally considered an easier task than the detection of Arg1 .


We used this tool because the output of CRF++ is compatible to CoNLL 2000 chunking shared task, and we view our task as a discourse chunking task.

linear-chain CRFs for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position.


As Figure 3 and Figure 4 show, the performance was competitive among the three lasso methods, when the number of features were small.

with a large number of features, relational lasso generally outperforms the other lassos.


Therefore, the computational complexity of relational lasso will not change even with α within the regularizer, and the overall speed of relational lasso is almost the same as that of the conventional method.

adaptive lasso requires twice as much time since the bottleneck part is done twice.


As an practical example, in an classification experiment on 492 617 training documents, 275 364 test documents and 132 199 categories of Yahoo!, ScutTD costs only 2.1 hours on training and 0.12 hours on classifying, while 1-vs-Rest costs 310 hours on training and 54 hours on classifying .

scutTD has a well-known deficiency of classification accuracy, that is, its performance is generally worse than the flat 1-vs-Rest approach (Bennett and Nguyen, 2009;Ceci and Malerba, 2007a;Xue et al., 2008).


One kind is mandatory leaf-node classification where only the leaf nodes are the validate labels or classes (Dumais and Chen, 2000;Freitas and de Carvalho, 2007;Silla and Freitas, 2010).

the other is non-mandatory leaf-node classification correspondingly, where both the internal nodes and the leaf nodes are validate labels (Lewis et al., 2004;.


According to Mani (2001), automatic text summarization takes a partially-structured source text from multiple texts written about the same topic, extracts information content from it, and presents the most important content to the user in a manner sensitive to the user's needs.

to summarizing one document that is termed as single document summarization, multi-document summarization deals with multiple documents as sources that are related to one main topic under consideration.


Given a sentence (or query 5 ), we first parse it into a syntactic tree using a parser like (Charniak, 1999) and then, calculate the similarity between the two trees using the tree kernel (discussed in Section 4.1).

syntactic information is often not adequate when dealing with long and articulated sentences or paragraphs.


For example, "I worked till 5pm yesterday" uses the past-tense verb "worked" to describe an event in the past.

in languages such as Chinese, no verb inflections exist to indicate any tense information (Xiao and McEnery, 2002).


(Ye and Zhang, 2005) also used a parallel Chinese-English corpus to obtain tense information, however, it was done manually.

our method is automated, which allows us to utilize a large amount of existing parallel data.


In practice, we also need to choose a training regime (in order to learn the weights of the formulae we added to the MLN) and a search/inference method that picks the most likely set of ground atoms (PA relations in our case) given our trained MLN and a set of observations.

implementations of these methods are often already provided in existing Markov Logic interpreters such as Alchemy 4 and Markov thebeast.


The intuition behind the previous formulae can also be captured using a local classifier.

markov Logic also allows us to say more: isArg (a) ⇒ ∃p.∃r.role(p, a, r) In this formula, we made a statement about more global properties of a PA relation extraction that cannot be captured with local classifiers.


are much more common than intra-sentential zero-anaphoric PA relations (Zero-Intra).

in Japanese, we often find zero-anaphoric PA relations called case ellipsis.


Another error is underlined that ga-case of "�� (transport by air)" is identified as "�� (reason)", because "��" is only a phrase dependent on "� �".

global improved the errors as � role(5, 6, ga), role(5, 4, wo), role(8, 6, ga), role(8, 10, wo), role(11, 6, ga), role(11, 10, wo) � .


This means that, for instance, any occurrence of the verb charge, such as in the expressions charge a fee or charge a battery, is assigned the same vector representation, ignoring the difference of word sense.

the fact that charge and impose are near-synonyms in charge/impose a fee will not be properly reflected in their respective meaning vectors, since the former, but not the latter, includes (context words reflecting) the "supply electricity" sense of charge.


Although all models have been evaluated on test-sets derived from the LST dataset in essentially the same way, the datasets differ slightly due to technical details, so strictly speaking the results cannot be compared directly.

since all authors report similar  30.0 † Cited from Erk and Padó (2010).


Dimension Title, Purpose and Gap-Background models present good results and should be incorporated to SciPo as new functionalities.

taking into consideration the annotation process, we observed difficulties to label the sentences with regard to the Dimension Linearitybreak.


However, the aforementioned line of work tackled subjectivity detection either as supervised or semi-supervised learning, requiring labelled data and extensive knowledge which are expensive to acquire.

both subjectivity and sentiment are context sensitive and in general quite domain dependent (Pang and Lee, 2008), so that classifiers trained on one domain often fail to produce satisfactory performance when shifted to new domains (Gamon et al., 2005;Blitzer et al., 2007).


The formal definition of the subjLDA generative process is as follows: In practice, it is quite intuitive that one classifies a sentence as subjective if it contains one or more strongly subjective clues (Riloff and Wiebe, 2003).

the criterion for classifying objective sentences could be rather different, because a sentence is likely to be objective if there are no strongly subjective clues.


The previously proposed DiscLDA (Lacoste-Julien et al., 2008) and Labeled LDA (Ramage et al., 2009) also utilize a transformation matrix to modify Dirichlet priors by assuming the availability of document class labels.

we use word prior sentiment as supervised information to modify the topic-word Dirichlet priors.


In addition, except for objective recall, subjLDA outperforms LDA in both the sentence and document modes for all the other evaluation metrics, with more balanced objective and subjective F-measures being attained compared to the other two models.


Moreover, apart from subjectivity clues, they also used additional features such as subjective/obejctive pattern and POS for the Naive Bayes sentence classifier training.

the proposed subjLDA model is relatively simple with only a small set of subjectivity clues being incorporated as prior knowledge.


Analyzing the objective recall and precision shown in Figure 3(b) and 3(c) reveals that, while incorporating the 4,500 least frequent neutral words considerably increases the objective recall, the objective precision does not drop much which eventually leads to the overall improvement of all the three models.

compared to the subjective words, the classification improvements by incorporating additional neutral words are less significant.


Here, we adapted the general keyphrase extraction procedure from the scientific publications domain (Witten et al., 1999;Turney, 2003) to the extraction of opinion-reasoning features.

our task is rather different since we aim at identifying the reasons for opinions, instead of keyphrases that represent the content of the whole document.


As we treated the mining of pros and cons as a supervised keyphrase extraction task, we conducted measurements with KEA (Witten et al., 1999), which is one of the most cited publicly available automatic keyphrase extraction system.

we should note that due to the fact that our phrase extraction and representation strategy (and even the determination of true positive instances to some extent) slightly differs from that of KEA, the added values of our features should rather be compared to our second Baseline System (BL W N ) which uses WordNet for candidate phrase normalization.


Table 2 contains the results of the strict evaluation for both domains.

since strict evaluation is more likely to suit the evaluation of scientific keyphrase extraction better, i.e.


For example, in "this camera uses a lot of battery power", "battery power" clearly indicates battery life, which is an aspect of the camera entity.

there are some important differences between resources and other types of aspects.


The process continues until no more resource terms or verbs can be found.

this simple strategy has some major problems.


Using SDRT as a formal framework, we have the following discourse relations: Contrast(a,b) in (1) marked by although, Continuation(a,b) in (2) marked by and, and Attribution(a,b) in (4).We observe in our corpus that segments related by a Contrast or Continuation relation often share the same subjective orientation (about 80 %).

discourse connectors are not the only indicator for deciding whether a segment is opinionated or not.


Indeed, the longer a text is, the higher difficulty for human subjects is in detecting discourse context in longer texts.

the study of this hypothesis falls out of the scope of this paper and is therefore left for future work.


In the Chinese dictionary, there are more than 800 words which begin with the same character "" meaning "one", which indicates that the corresponding node will have more than 800 children.

in an English trie, the number of child nodes for any node doesn't exceed 52 for there are only 26 letters in the alphabet.


In Method 1, we make code mapping according to their original order in Unicode.

different characters have different frequencies in the dictionary, then what will happen if we map two characters with the same frequency to two target jump codes of which the numerical difference is as small as possible?


It spreads from 80 (hex) to 3730 (hex), for there are more than 14,000 Chinese characters are used in the dictionary.

in a English doublearray, the jump codes vary in a much smaller interval, which is from 41 (hex of letter "A") to 7A (hex of letter "z") originally.


achieve a CR larger than 95% and reduce the space usage by a percentage near or even larger than 40%, which is similar to Method 3.

method 6 is slightly better than method 5 from the aspect of space usage, just like method 2 is slightly better than method 1.


This fact seems to contradict our intuition that modal verbs are not of high diversity with respect to a complementary set containing nominal words only, which are generated according to determiners.

we know that, at least in English, words are of ambiguous categories; therefore, those words of both nominal and verbal senses, such as 'consider' and 'help', are classified to the complementary set by determiners according to their nominal sense, but their existence in the complementary set also enlarges the diversity of modal verbs due to their verbal sense.


With the rapid growth of the Internet data and the dramatic changes in the user demographics especially among the non-English speaking parts of the world, machine transliteration is important in many cross-lingual NLP, MT and CLIR applications as their performances have been shown to positively correlate with the correct conversion of names between the languages in several studies (Demner-Fushman and Oard, 2002;Mandl and Womser-Hacker, 2005;Hermjakob et al., 2008;Udupa et al., 2009).

the traditional source for name equivalence, the bilingual dictionarieswhether handcrafted or statistical built offer only limited support because new names always emerge.


Nivre (2008); Huang and Sagae (2010)), mainly because it achieves state-of-the-art accuracy while retaining linear-time computational complexity, and is also considered to reflect how humans process natural language sentences (Frazier and Rayner, 1982).

although some of the Chinese POS tags require long-range syntactic information in order to be disambiguated, to the extent of our knowledge, none of the previous approaches have addressed the joint modeling of these two tasks in an incremental framework.


Given a segmented sentence, our model simultaneously considers possible POS tags and dependency relations within the given beam, and outputs the best parse along with POS tags.

the combined model raises two challenges: First, since the combined search space is huge, efficient decoding is difficult while the naïve use of beam is likely to degrade the search quality.


In practice, just remembering a minimal set of features called kernel features˜ f (j, S) suffices to evaluate the equivalence of states: (2) By merging equivalent states based on this condition, we only need to remember relevant information from the top d (d = 3 in our models) trees on the stack to evaluate the score of the next actions.

since the stack shrinks when a REDUCE-LEFT/RIGHT action is applied, you often need to recover the last element of the stack from the history.


the optimality of the deductive system) is still assured (proof omitted due to limited space) even with the delayed features incorporated.

because any number of REDUCE-LEFT/RIGHT actions can occur between two SHIFT actions, the delayed features might need to refer to unboundedly deep elements from stack trees; therefore, the boundedness (see Huang and Sagae (2010)) of the kernel features no longer holds and the worst-case polynomial complexity is not assured.


Thanks to these features, we added to LGLex 2,084 adverbial entries (+20%).

a certain number of paraphrases are part of construction features, and thus need to be extracted from them.


Because the head words for the internal nodes are not marked in the PS, there are several possibilities in choosing the head words for the internal nodes: the head word of the Y can be a or b, and the head word of X can be c or the head word of Y , resulting in four possible DSs, as shown in Figure 3.

no matter which head words we choose for the internal nodes in the PS, the resulting DSs will not be the ones in Figure 4.


According to the structuralist tradition, coordination is an endocentric construction, since it contains not only one but several heads that can replace the whole construction syntactically.

this raises the question of whether coordination can be analyzed in terms of binary relations holding between a head and a dependent.


On the other hand, rather than the approximated classfier-based approach to labeling, one could consider settling for an exact but partial labeling by only assigning those dependency labels which unambigously arise from tuples of head and dependent CDG types.

the dependency labels obtainable with absolute certainty in this way are often of the less interesting kind -e.g.


Thus LPCFG becomes sensitive to lexical heads, and its performance is improved.

the information provided by lexical heads is limited.


The number of iterations needed is optimized on the development set.

introducing sequential dependency into this lexical model would cause a severe efficiency problem with the joint inference for parsing.


The lexical model I enables the parsing to utilize a large variety of features other than those encoded in the CFG in use.

the computation of forward/backward variables is expensive in both time and space.


The surrounding words provide most information for the disambiguation for tagging.

the weight parameter λ seems not as effective as expected for English.


This approach has the advantage to combine heterogeneous models, and solves the complex combinatory optimization problem via Lagrangian relaxation.

it has a inevitable defect of inefficiency, since it requires to parse and tag the input sentence repetitively (usually 10 times for each sentence).


This has a two-fold benefit: i) introduces a generalization level over surface forms; ii) provide to the parsing algorithm only the essential information, since POS tags are directly related to syntactic constituents and are sufficient to induce the syntactic structure of a sentence.

in our system, the role played by POS tags in syntactic parsing is played by entity components, annotated by CRF.for named entity annotation it is not true that components are sufficient to induce the entity tree.


For example, the 1 st stage tree for sentence 3 is shown in Figure 4 (a).

a normal single stage parser (our baseline parser) is trained on the full tree that looks like Figure 4 (b).


2-Soft, though giving a minimal improvement in the accuracies, is not statistically significant with the baseline.

on analyzing the output parses of all the three setups, we found clear and similar improvement patterns (listed below) in case of both 2-Hard and 2-Soft.


This seems to contradict with the fact that the latter solves the problem optimally while the former only looks for a reasonable solution.

this is not a problem because our word-reordering model itself cannot catch accurately the quality of a sentence.


Most SMT systems, not only phrase-based models (Och and Ney, 2004;Koehn et al., 2003;Xiong et al., 2006), but also syntax-based models (Chiang, 2005;Galley et al., 2006;Huang et al., 2006;Shen et al., 2008), usually extract rules from word aligned corpora.

these systems suffer from a major drawback: they only extract rules from 1-best alignments, which adversely affects the rule sets quality due to alignment mistakes.


This is different for the conditional models, which are easier to handle but where most approaches are based on initializing from single-word based models (Brown et al., 1993;Vogel et al., 1996;Al-Onaizan et al., 1999).


Superficially the Bi-HMM looks similar to (Deng and Byrne, 2005).

this latter is actually a Mono-word model.


In principle, each of the three arising distributions has its own parameter set.

the initial probability and the inter-alignment model share the parameters p 0 and p 1 .


The technique presented in this paper is related to these previous works as it concerns the weighting of corpora or sentences.

it does not 1996 1997 .......................... 2009 2010 Alignments obtained from time-stamped training parallel text  Figure 1: Overview of the weighting scheme.


(2010) to automatically optimize the weights of each time period.

this approach does not seem to scale very well when the number of individual corpora increases.


The best performance is obtained when using all the data (55M words, BLEU=30.48), but almost the same BLEU score is obtained by using only the most recent part of the data (24M words, part Recent 2).

if we use the same amount of data that is further away from the time period of the test data (25M words, part Ancient 2), we observe a significant loss in performance.


The research community has been using methods such as word error rate, EER, precision and recall and its many variants as metrics to evaluate systems.

due to homonyms and phone-set differences across multiple languages, word error rate is not always sufficient to distinguish transliteration accuracy.


This enables the use of taskspecific loss functions (e.g BLEU).

the definition of Bayes Risk depends critically on the posterior probability of hypotheses.


Other works based on salient terms are frequent phrases by (Osinski and Weiss, 2005), and integration of hierarchical information by (Muhr et al., 2010).

the suggested terms, even when related to each other, tend to represent different aspects of the topic underlying the cluster, and it is often the case that a good label does not occur directly in the document.


They showed the effectiveness of the method.

wikipedia is the free online encyclopedia, and everyone can access and edit the information.


Each prediction takes the form of a probability distribution that is provided to an encoder, which is usually an arithmetic coder.

the details of actual coding technique are of no relevance to this paper.


Probability ρ i (c) is expected to be a strong clue for sense disambiguation.

it is not clear how we can effectively use the probabilities of different cases c; ρ i (c) for some cases would be reliable, while some others less reliable.


Historically, using word senses usually involved the use of manually compiled resources in which word senses were represented as a fixed list of definitions.

there seem to be some disadvantages associated with such fixed list of senses paradigm.


These context vectors are clustered and the resulting clusters are taken to represent the induced senses.

when constructing context vectors, the approaches based on VSM assume that the words occurring in the contexts are independent and do not exploit semantic relevance between words.


Just like what have been shown in (Agirre and Soroa, 2007) and (Manandhar et al., 2010), the 1c1w baseline shows the best performance.

it only discoveries one sense for each target word, which is the most frequent sense of the target word.


However, it only discoveries one sense for each target word, which is the most frequent sense of the target word.

wSI_SR and wSI_SOV predict 2.1 and 2.4 senses on average per word respectively, which is more close to the actual number of senses (2.5).


For example, specific attributes like power consumption, pulsator, load, spin-dry effectiveness, noise, water usage, water leakage, etc for a product like washing machine cannot be correctly found in descriptions.

customers express their opinions in the form of reviews.


In general, we can see that the classifiers perform relatively well for the majority categories, such as problems and solutions.

for minority classes, e.g., "feedback" (that is only 9.8% among all the posts), the classifiers are not able to learn well.


Of course, it is possible to compile comprehension rate at the sentence level by preparing comprehension questions for each sentence, but this is just not realistic.

we have to compile read and listened data at the sentence level just like for spoken and written data.


In these cases, answerers did not indicate unclear points of questions.

we think these unhelpful solutions are one type of indication of unclear points of questions.


Normally, the retrieved questions are ranked according to the semantic similarity to the query question.

taylor (1962) argues that the user may fail to express his infoNeeds fully in the question.


Non-standard spellings in text messages often convey extra pragmatic information not found in the standard word form.

text message normalization systems that transform non-standard text message spellings to standard form tend to ignore this information.


Sometimes it may be composed of a number of sentences.

to annotate in such a way is costly and time-consuming.


We can see that the POS tagger can effortlessly assign the right tags to both "development" and "develop" in the English side.

it is very difficult in the Chinese side since no word form inflection is available and the context features may be too sparse or uninformative.


However, it is very difficult in the Chinese side since no word form inflection is available and the context features may be too sparse or uninformative.

the introduction of long-distance dependencies can largely reduce this difficulty.


They use the same WordNet-based relatedness method in order to expand documents, following the BM25 probabilistic method for IR, obtaining some improvements, specially when parameters had not been optimized.

to their work, we investigate methods to apply relatedness to query expansion, and we compare the results with pseudo-relevance feedback.


Currently, corpora are created mainly through manual annotation by expert annotators.

the cost of annotating a necessary size of corpora is prohibitive.


For example, SemCor 1 corpus contains WSD annotation of about 250,000 words sampled from a subset of the Brown corpus.

for most of the ambiguous words, the number of examples is still too small to train a high performance all-words WSD model.


The method can take advantage of a larger range of people.

human computation methods are rarely used in natural language processing tasks.


(2012) proposed an approach for adapting an answer extractor trained on one domain to another, by separating out the lexical characteristics of an answer from its domain relevance.

learning the lexical characteristics still required a training set.


The intuition is that posts that bear more resemblance to other posts in the thread have higher chances of being answers.

in a lot of discussion forums, especially those related to troubleshooting and problem resolution, we found that this assumption usually does not hold.


One of the methods proposed in this paper that uses a parallel acknowledgment classification task, belongs to the family of Multi-Task Learning (MTL) (Caruana, 1997) since what is learned for each task is used to improve the other task.

to the best of our knowledge, this is the first work that proposes a MTL-type answer classifier for forums in a semi-supervised setting.


This does not come as a surprise as the supervised classifier learns from labeled training data while the rule-based classifier is unsupervised.

we also find that the precision of the rule-based classifier largely outperforms our best supervised classifier on HLTH.


The fact that the best overall F-score achieved is not higher may be ascribed to the heavy noise (spelling/grammar mistakes) contained in our web-data.

we believe that even with those data we can show the relative effectiveness of the different feature types which is the most relevant aspect in our proof-of-concept investigation.


Most previous work on QS only can use wordbased MI as introduced above.

in some cases, the MI between tokens can not provide sufficient information for a segmentation decision.


investigated the use of syntactic dependency output by a dependency parser and reported a slight improvement over a baseline method that used only words.

the use of dependency parsers still introduces the problems stated in the previous section because of their handling of only syntactic dependencies.


At each node, we select children to which edge classifiers return positive scores (Lines 4-7).

if no children have positive scores, we select one child with the highest score (Lines 8-10).


English particles only, for example, also, especially, principally) mostly precede a focus element and in the theory of TFA, they are also considered contextually nonbound.

also contrastive contextually bound expressions can follow the rhematizerstypically at the beginning of the sentence (and in this case, also the rhematizers are contextually bound).


Lexical resources like wordnet usually feature animacy of nominals of a given language (Fellbaum, 2010;Narayan et al., 2002).

using wordnet, as a source for animacy, is not straightforward.


x is the normalized dimensions in a feature vector of a nominal x. k is the number of coordinates and x i is the i th coordinate of x. Animacy is an inherent and a non varying property of entities that nominals refer to.

due to lexical ambiguity animacy of a nominal can vary as the context varies.


The cluster prototypes, returned by the fuzzy clustering, show animates are left skewed while inanimates are right skewed on the hierarchy of control.

in our clustering experiments the order of dative/accusative and instrumental case markers on the control hierarchy (Scale 1) has been swapped.


• Nominal Ambiguity: As a matter of fact, animacy is an inherent and a non-varying property of nominal referents.

due to lexical ambiguity (particularly metonymy), animacy of a word form may vary across contexts.


We have addressed this problem by capturing the mixed membership of such ambiguous nominals.

since animacy of a nominal is judged on the basis of its distribution, the animacy of an ambiguous nominal will be biased towards the sense with which it occurrs in a corpora.


Such transformation is of course not possible in general.

we found that a greedy rewriting procedure suffices for that purpose on all of the high-school level math problems used in the experiment.


The problems containing only six variables may be hard for today's computer with the best algorithm known.

several positive results have been attained as the result of extensive search for practical algorithms during the last decades (see (Caviness and Johnson, 1998)).


This suggests that there is a sizable subjective aspect to these judgments and we should be somewhat skeptical of the judgment of any particular annotator.

we had forced our annotators to make a boolean choice for each style, which may be somewhat inappropriate for somewhat non-discrete phenomenon like style.


Another shortcoming of previous classification approaches is that they only focus on detecting the overall topical category of a document.

they do not perform an in-depth analysis to discover the latent topics and the associated document category.


In the same way violence analysis maps violence polarity into violence words such as looting, revolution, war, drugs and non-violent polarity to background words such as today, happy, afternoon.

as opposed to sentiment and affect prior lexicon derivation, the generation of violence prior lexicons pose different challenges.


Our intuition is that low entropy words are indicative of semantically coherent topics and therefore more informative, while high entropy words indicates words whose usage is more topical diverse and therefore less informative.

the entropy of word w given the class label c is defined as follows: where C denotes the number of classes (in our case violent and non-violent) and p(w|sd c i ) denotes the probability of word w given the document sd i in class c. to the general E SD , the class word entropy characterises the usage of a word in a particular document class.


For the case of non-violent topics, VDM revealed topics which appeared to be less semantically coherent than those of violent topics.

when reading the non-violent VDM T1, it gives an insight of the super bowl game related to the Jets.


Furthermore, when the spammer's intention is just advertising, we can easily identify signs of its activity: repeated phone numbers or URLs and then ignore them.

when the spammer's intention is to obtain higher reputation within the community, the spam content may lack obvious patterns.


Their method only works for definition sentences, where the assumption that the formal and informal equivalents cooccur nearby holds.

this assumption does not hold in general social network microtext, as people often directly use informal words without any explanations or definitions.


Li and Yarowsky (2008a) computed the Levenshtein distance (LD) on the Pinyin of the two words in the pair to reflect the phonetic similarity.

as a general string metric, LD does not capture the (dis-)similarity between two Pinyin pronunciations well as it is too coarse-grained.


As an initial step, we can recognize informal words and segment the Chinese words in the sentence by applying joint inference based on a Factorial Conditional Random Field (FCRF) methodology (Wang and Kan, 2013).

as our focus in this work is on the normalization task, we use the manually-annotated gold standard informal words (O) and their formal equivalents (T ) provided in our annotated dataset.


Note that this constraint makes our method more efficient over a brute-force approach, in exchange for loss in recall.

we feel that this trade-off is fair: by retaining the top 1000 candidates, we observed the loss rate of gold standard answers in each of the channels is 14%, 15%, and 17% for phonetic substitution, abbreviation and paraphrase, respectively.


Assuming the character-word mapping events are independent, we obtain: where o i (t i ) refers to ith character of O (T ).

this SCM model suffers serious data sparsity problems, when the annotated microtext corpus is small (as in our case).


In this case, Fj may be a better one because it happens to be the representative node for the path between Fi and L1.

there is a chance that the sub-tree of Fi may have important features (i.e., L3, L5) that end up elevating Fi's weight unfairly.


Transliteration methods have often been used for the task of keyword matching across different languages (Chen and Ku, 2002;Fujii and Ishikawa, 2001).

han (2006) applied the transliteration method to perform part-of-speech tagging for Korean texts using Xerox Finite State Tool.


Summarization of properties in Korean SMS text Ling and Baron (2007) reported that lexical shortening is the one of the most significant characteristics one can see in text messages.

'ignoring spacing' is the exception, since Korean suffixes can play as good predictors for the roles or the functions of the preceding stem.


Thus one would anticipate an increase in errors when the input text is not properly spaced, because it would increase the complexity of the analysis process.

unlike the predictions, the Romanization-based method recorded a lower precision than the morphological analysis-based approach.


This is because the shortened targets can also be found as sub-string of bigger words.

this shortcoming does not weaken the efficiency of the whole approach.


As mentioned earlier, past studies paid little attention to elaborating the lattice generation algorithm.

the results of our experiments reveal that the design of the lattice generation algorithm crucially affects the performance of the reranking system (including speed, accuracy, and lattice size).


Most previous work on this approach has aimed at developing a single general-purpose unknown word model.

there are several types of unknown words, some of which can be easily dealt with by introducing simple derivation rules and unknown word patterns.


There are a lot of studies on rendaku in the field of phonetics and linguistics, and several conditions that prevent rendaku are known, such as Lyman's Law (Lyman, 1894), which stated that rendaku does not occur when the second element of the compound contains a voiced obstruent.

few studies dealt with rendaku in morphological analysis.


The wide scope of influence is due to the fact that " " consists of hiragana characters like most Japanese function words.

in the case of (5), although the unknown word " " (decontamination) is divided into two parts by JUMAN, there is no influence on the adjacent analyses.


UniDic dictionary (Den et al., 2008) handles orthographic and phonological variations including rendaku and informal ones.

the number of possible variations is not restricted to a fixed number because we can insert any number of long sound symbols or lowercases into a word, and thus, all the variations cannot be covered by a dictionary.


is input into the baseline system, it builds the word lattice that is described with solid lines in Figure 2.

this lattice does not include such expressions as " " and " " since they are not included in the lexicon.


The number of potential entries of onomatopoeias with repetition is large, but the candidates of onomatopoeias with repetition can be quickly searched for by using a simple string matching strategy.

to search the candidates of onomatopoeias without repetition is a bit time consuming com- Input : "ᬂᬼᬂᬼᯯ" (Approximately how much?)


Since long sound symbols and lowercases rarely appear in the lexicon, there are few likely candidates other than the correct analysis.

voiced characters often appear in the lexicon and formal   text, and thus, there are many likely candidates.


Our system recognized unknown onomatopoeias with repetition at a recall of 89%, which is not very high.

since there were several repetition expressions other than onomatopoeias, such as " / " (wow wow) as shown in Table 9, we cannot lessen the cost for onomatopoeias with repetition.


For example, previous work has shown that sequence models alone cannot deal with syntactic ambiguities well (Clark and Curran, 2004;Tsuruoka et al., 2009).

stateof-the-art systems usually utilize high complexity models, such as lexicalized PCFG models for syntactic parsing, to achieve high accuracy.


Attempts have been made to utilize hand annotated semantic information for constituency parsing (Fujita et al., 2007;MacKinlay et al., 2012) as well as dependency parsing (Øvrelid and Nivre, 2007;Bharati et al., 2008;Ambati et al., 2009).

acquiring such information for new sentences remains a challenge.


It may look intuitive to use fully expanded concept lineage, as it contains more detailed description of the lexical unit.

opting for a highly fine-grained concept lineage leads to the problem of sparseness.


As depicted in Graph (Figure 4), there is a average increase of 0.38 (LAS) on all datasets using the first sense strategy from the baseline.

using WSD the accuracy decreased across all datasets.


This is evidence that our strategy of splitting experiments into basic and extended was sound for these TDs.


For less frequent POS classes (those that dominate the macroaveraged measure, especially verbal POS), sequence information and "long-distance" context is probably more stable and can be exploited better than for NN, NNP and JJ.

there is still a dropoff from the baseline for BIO; we attribute this to the larger differences in the transition probabilities for BIO vs WEB (Table 4); the sequence classifier is at a disadvantage for BIO, even on a macroaveraged measure, because the transition probabilities change a lot.


Third, there are languages that abound in verb + noun constructions or multiword verbs (such as Estonian (Muischnek and Kaalep, 2010) or Persian (Mansoory and Bijankhan, 2008)): verbal concepts are mostly expressed by combining a noun with a light verb (Mansoory and Bijankhan, 2008).

there are views that the relationship between the verbal and the nominal component is not that of a normal argument.


As can be seen, we have the light verb construction döntést hoz decision-ACC bring "to make a decision" in the sentence.

it is parsed as a "normal" object of the verb in the first case (OBJ) and as a light verb object (OBJ-LVC) in the second case.


Thus, there is no LVC in the above example, but approaches that heavily build on MWE lexicons may falsely identify this verbobject pair as a light verb object-light verb pair since they hardly consider contextual information.

dependency parsers have access to information about other dependents of the verb hence they may learn that in such cases the presence of a dative dependent argues against the identification of the verb-object pair as an LVC.


Hence, the cognitive process behind labeling a participant to have a particular type of power is not a binary decision the annotator makes for each participant.

evaluating agreement on such a formulation is not straightforward.


does not understand (1) OOV words, e.g., "the opposite wall" * ; (2) more than one meaning of polysemous positional relations, e.g., "to the left of the table" * as "to the left and on the table" as well as "to the left and next to the table"; (3) positional relations that are complex, e.g., "in the left near corner of the table" * , or don't have a landmark, e.g., "the ball in the center" * ; and (4) descriptive prepositional phrases starting with "of" or "with", e.g., "the picture of the face" * and "the plant with the leaves" * .

contextual information sometimes enables the system to overcome OOV words.


The relevance measure fc (Equation 2), which is applied to both metrics, enables us to handle equiprobable interpretations.

rank-based evaluation metrics do not consider the absolute quality of an interpretation, i.e., the top-ranked interpretation might be quite bad.


Accuracy over segments was higher, at 96.26% for texts, and 87.28% for ASR outputs.

ssP's performance for the identification of Noise was rather poor, with an average accuracy of 54.75%.


The probability of the Semantic Model for this modified sentence is which is higher than that of the original Semantic Model, as is the probability of the new Text given the new Semantic Model: Pr(Text |SemModel ) = Pr("the thing"|O) Pr("in"|P ) Pr("the microwave"|L) .

this gain is offset by the penalties incurred by the modifications.


Note that, as the number of follow up questions presented increases, the scores will improve since it is more likely that the 'correct' choice is presented.

there is a trade-off here since the agent has to again peruse more questions, which increases time spent, and so we limit this value to 5 as well.


An example of such efforts is the automatic translation of English Wikipedia to languages with smaller collections.

mT quality is still far from ideal for many of the languages and text genres.


The selected summarized sentences are then translated to the target language using Google Translate.

we go a step further and design a hybrid approach in which we incorporate our MT quality classifier into the state-of-the-art summarization system.


Most of existing studies dealing with comparable corpora look for parallel data at the sentence level (Zhao and Vogel, 2002;Utiyama and Isahara, 2003;Munteanu and Marcu, 2005;Abdul-Rauf and Schwenk, 2011).

the degree of parallelism can vary considerably, from noisy parallel texts, to quasi parallel texts (Fung and Cheung, 2004).


The bootstrapping algorithm can effectively reduce manual intervention in building the system.

it is prone to noise brought in during iterations.


Compared with Table 2 above, we can find that the coverage of the English NEs is lower than that on Chinese.

the volume of the extracted NEs is al-   most 30 times larger.


Yom-Tov and Diaz (2011) report that Twitter can broadcast news faster than traditional media, which provides an opportunity for event detection in Twitter.

there are challenges in event detection from Twitter data: 1) tweets are too short and sometimes cannot carry enough information; 2) tweets contain many noisy words, which can be harmful for event detection and 3) the volume of Twitter data is very large, which makes event detection a big data problem.


Both similarity of edges and Wikipedia are useful in filtering out heterogeneous collections from news events, while Wikipedia can also separate some news and topics.

there are several problems with this approach: 1) newsworthiness cannot distinguish news from some topics, includes horoscope topics ("sagittarius; approach; big trouble") and topics such as "hitler; fox; megan fox; rip; megan; selena gomez", which contain segments that can also frequently occur in Wikipedia; 2) as a single measure, newsworthiness is subject to a tradeoff between precision and recall, while a high precision can be obtained only with an extremely low recall (about 10%).


However, most of the above work focuses on relation extraction rather than event extraction.

to the well-studied problem of relation extraction, only a few works focused on event extraction.


Secondly, since only verbs with subject and object are extracted, non-predicate verbs and the verbs without subject/object will not be extracted as candidate triggers.

the coverage of possible triggers by our trigger extraction algorithm is reasonable good (more than 85%), because most of the trigger words appear repeatedly in the corpus, and their usages are varied.


Therefore, it takes domain specific event triggers as the input.

it is also a costly task to annotate triggers for new domains.


For training and evaluating the performance the proposed method, we need a large number of abbreviation and corresponding standard form pairs.

manually labeling is a laborious and time consuming work.


We think that the global formulas contribute a lot for the entire accuracy.

since the constraint of simultaneously dropping or keeping characters does not consider context, it may also bring some false matches.


Comparing the performances of CRFs and MLNs, we can observe that CRFs achieve slightly better performance in classifying single characters.

mLNs achieve significantly better results of the entire accuracies.


used linguistically motivated statistical measures to distinguish subtypes of verb + noun combinations.

it is a challenging task to identify rare LVCs in corpus data with statistical-based approaches, since 87% of LVCs occur less than 3 times in the two full-coverage LVC annotated corpora used for evaluation (see Section 3).


In some other studies (Cook et al., 2007;Diab and Bhutada, 2009) the authors just distinguished between the literal and idiomatic uses of verb + noun combinations and LVCs were classified into these two categories as well.

to previous works, we seek to identify all LVCs in running texts and do not restrict ourselves to certain types of LVCs.


The resulting datasets were not balanced and the number of negative examples basically depended on the candidate extraction method applied.

some positive elements in the corpora were not covered in the candidate classification step, since the candidate extraction methods applied could not detect all LVCs in the corpus data.


In this dataset, only one positive or negative example was annotated in each sentence, and they examined just the verb-object pairs formed with the six verbs as a potential LVC.

the corpus probably contains other LVCs which were not annotated.


This can be explained if we recall that this dataset applies a restricted definition of LVCs, works with only verb-object pairs and, furthermore, it contains constructions with only six light verbs.

wiki50 and SZPFX contain all LVCs, they include verb + preposition + noun combinations as well, and they are not restricted to six verbs.


released a new version, the op spam v1.4 dataset, with gold-standard negative reviews included as well (Ott et al., 2013), which offers an opportunity to more extensively study the problem of detecting deceptive reviews.

the work described in this paper is focused on positive review spam, and based directly on the work of Feng et al.


N-GRAM features: As shown by Ott et al., the bag-of-words model is effective for identifying deceptive reviews.

the optimal choice of n is not consistent in Ott et al.


Intuitively, in order to define a new epoch, both a big social impact of a series of events and new issues, which arouse the social interest, must be observed.

it is hard to define what makes a feature "distinctive" or an event a "great change".


They show that it is possible to determine censorship and suppression by comparing the frequencies of proper names in bilingual Google books corpora.

the authors did not proceed to a systematic studies of epochs.


We can see that the number of terms which are positive to statistical tests varies substantially.

it is not by chance that the changes occur.


Various claims have been made about social media text being "noisy" (Java, 2007;Becker et al., 2009;Yin et al., 2012;Preotiuc-Pietro et al., 2012;Eisenstein, 2013, inter alia).

there has been little effort to quantify the extent to which social media text is more noisy than conventional, edited text types.


Most research to date on social media text has used very shallow text processing (such as keyword-based time-series analysis), with natural language processing (NLP) tools such as partof-speech taggers and parsers tending to be disfavoured because of the perceived intractability of applying them to social media text.

there has been little analysis quantifying just how hard it is to apply NLP to social media text, or how intractable the data is for NLP tools.


There has been work in the NLP community to detect arguments and interruptions in spoken as well as written interactions (Somasundaran et al., 2007).

the wellstructured nature of interactions that is expected in the debates allows us to use some simple heuristics to detect arguments and interruptions for the purposes of this study.


It is hard to infer strong conclusions based purely on the SVM feature weights.

sVM does pick up some interesting signals.


SSR These results indicate that there is no one readability index which correlates significantly with all of the linguistically motivated complexity features.

it seems that they complement each other well as each one of them is significantly correlated with a different subset of features.


it can be identified in texts with the help of semantic tools.

there are other types of uncertainty which cannot be described by just concentrating on semantics.


On the number of different cues, Table 1 tells us that the set of linguistic cues expressing weasels are the most limited, with almost 100 cues.

peacock cues vary the most with 540 cues.


This prevents us using those corpora in the evaluation.

aCE also produced a multilingual small corpus called REFLEX Entity Translation Training/DevTest (REFLEX for short), which consists of about 60K of tokens with two levels of classes.


The most notable findings, as shown in Table  11 are that, the recall metric shows a sharp drop in all datasets.

the precision shows high scores, suggesting the Wikipedia corpus is strong in difference when compared with the news-wire domain.


Despite solid work on Arabic modality in theoretical linguistics (Mitchell and al-Hassan, 1994;Brustad, 2000;Moshref, 2012), there are no Arabic corpora annotated for modality, not even the widely used Penn Arabic Treebank.

there is a plethora of work and annotated corpora for modality in other languages, including English (Saurí et al, 2006;Baker et al., 2010;Rubinstein et al., 2013), Portuguese (Hendrickx et al., 2010;Avila and Mello, 2013), Japanese (Matsuyoshi et al.


However, the canonical word order in Tunisian verbal sentences is SVO (Subject-Verb-Object) (Baccouche, 2003).

the MSA word order can have the following three forms: SVO / VSO / VOS (2).


For example, if the word >أكتب />ktb /Write in MSA is preceded by a negative particle such as /لمlam (Do not), the verb in the dialect will be: mAktibti$/=ماكتبتش TUN-Neg-Particle(+)ما Tun-verb +)كتب( Tun-Neg-enc )ش( Tools words or Syntactic tools exist in a large amount in the Treebank and all MSA-texts.

their transformation was not trivial and required for each tool a study of its different contexts.


Data driven Machine Translation approaches have gained significant attention as they do not require rich linguistic resources such as, parsers or manually built dictionaries.

their performance largely depends on the amount of training data available (Koehn, 2005).


In many PBSMT systems, once the phrase-pairs have been extracted, it is no longer required to store the training corpus from which the phrase-pairs were extracted.

while dealing with many morphologically rich languages, the morphological variants of the target phrase not only depend on their source phrase but also on the context in which the source phrase appeared.


POS taggers are now easily available for most Indian languages.

no other rich sources such as, parsers or morphological analyzers are used on the target language.


• Uniform probability is not informative: The heuristic extraction method tends to assign an uniform probability for groups of translations and this is evident in the flat segments of the baseline curve and is especially dominant in the low probability region.


Semi-supervised methods, which make use of both labeled and unlabeled data, are ideal for sentiment classification, since the cost of labeling data is high whereas unlabeled data are often readily available or easily obtained (Ortigosa-Hernández et al., 2012).

there are some drawbacks of semi-supervised approaches such as most of the work assume that the positive and negative samples in both labeled and unlabeled data set are balanced, otherwise models often bias towards the majority class (Chawla et al., 2002;Yen and Lee, 2009).


(2) The sentiment scores will be updated to incorporate diverse measurements leading to less odd scores.

broader coverage did not necessarily guarantee better performance since irrelevant words often matched to generate noise.


Among semi-automatic methods, rule-based methods are known for high accuracy if the patterns are carefully chosen according to morphological structure or special format of corpus (Nakayama et al., 2008), either manually or via automatic bootstrapping (Hearst, 1992).

the methods suffer from sparse coverage of patterns in a given corpus.


We believe that the best topical key concept about this topic is 文艺 'literature'.

'literature' cannot be created if it never appears in the tags of Douban.com.


While these are efficient at retrieving synonymous words, they fare less well at identifying antonyms as non-similar words, and routinely include them as semantically similar words.

despite the problems resulting from this, there have only been few approaches that explicitly tackle the problem of synonym/antonym distinction, rather than focussing on only synonyms (e.g.


Figure 1: The N-gram language model treats the English sentence "all things pass" composed of (all, things) and (things, pass), for N = 2.

revealing the structure of a sentence is the task of parsing, which is based on linguistically oriented formulations and focuses on generating the likeliest structure for a given sentence.


There are also dependency-based approaches (Stolcke et al., 1997;Gao and Suzuki, 2003;Graham and van Genabith, 2010).

these approaches need a trained dependency parser because they construct a language model based on the decisive best structure produced by the parser.


To deal with this problem, the dependency model with valence (DMV) 6 proposed by Klein and Manning (2004) introduces a special mark STOP.

it is necessary to distinguish two kinds of parameters, P STOP and P CHOOSE in the bi-gram estimation, which makes it difficult to extend the approach to higher orders.


Comparing the test set perplexities in Tables 3  and 4, we can see the dependency bi-gram model achieves the same, or sometimes better performance of the original N-gram language models.

when we look at the tri-grams, the interpolated modified Kneser-Ney (KN) discounting method, which is state-of-the-art, shows its strength and our dependency model does not produce much improvement for the reasons we described above 18 .


learning the θ parameters in our case is not as straightforward, since the objective is non-differentiable at many points due to the 2 1 norm.


van Noord (2009) trades parsing efficiency for parsing effectiveness by learning a heuristic filtering of useful parses.

we develop a selfsupervised online learning algorithm to achieve efficient extraction without reducing effectiveness.


In our experiments, missing keyphrases have not been removed.

we evaluate with stemmed forms of candidates and reference keyphrases to reduce mismatches.


The approaches are studied in (Bakliwal et al., 2011;Bespalv et al., 2011;Kennedy and Inkpen, 2006;Jin et al., 2009;Narayanan et al., 2009;Pang et al., 2002;Schuller and Knaup, 2011).

the lexicon-based approaches are mostly unsupervised, requiring a dictionary or a lexicon of pretagged words.


The reviewer seems happy with the camera size, structure, easy use, video modes, SDHC support etc.

the autowhite balance and high compression leading to sensor acuity seem to disappoint him.


Mining infor-mation from ConceptNet can be difficult as oneto-many relations, noisy data and redundancy undermine its performance for applications requiring higher accuracy (Smith et al., 2004).

we use ConceptNet for the following reasons: 1.


For example, for a pair of words (seikaku ga warui) "bad personality", neither "bad", nor "personality" on their own express harmful meaning.

when these words are used together in a dependency relation, they become harmful (negative depiction of a person's personality).


In previous research the mixing was set at the same rate (half of the entries included cyberbulliyng).

it cannot be assumed that harmful entries appear in real life with the same rate as normal ones.


Since the phrases included seed words as well, this most probably increased the polarity value of the harmful entry.

there were many nonharmful entries classified as harmful with harmfulness polarity score equally high or higher than the actual harmful entries.


For instance, PR is interpreted by probable and PS is interpreted by possible.

in Japanese, it is not so straightforward to distinguish between PR and PS due to a diverse variety of modality expressions.


Out of the 1,128 correct instances in the area analyzed by the corpus, 351 are correct by predicates in the dictionary.

to this, only 34 errors are due to insufficient coverage for predicates.


In Section 3, we described that our analyzer determines the event factuality based on three components: Predicates, Functional Expressions, and Propagated Factuality.

we find that it is crucial to determine boundaries whether the analyzer should propagate the factuality.


This means that our approach based on lexical knowledge works well, especially for minor labels.


402 of them do not have CT+ as the propagated factuality and all of them should block the factuality propagation.

only 229 of 402 blocked the propagation.


We therefore performed an additional experiment with lexical knowledge for scope and discussed its helpfulness.

the issue regarding scope includes the issue by profound meaning and context.


The synsets are highly accurate in representing words' meanings.

the size of the thesaurus is limited, and not equally available in different languages.


(1a) shows that the entailment relation can be correctly predicted through recognizing "read into" → "interpreted" 2 and "what he wanted" → "in his own way".

the alignment developed in MT does not solve the non-alignment samples well.


Since the CWS rules can be trained from context without linguistic information, the proposed CWS method might also work for Chinese texts from different ages.

there are some issues and problems that require further investigation.


Furthermore, the fuzzy v measure that we developed in order to evaluate soft clusterings still seems to provide sub-optimal evidence of clustering quality: The magnitude of the score depends on the threshold, so it is difficult to decide which threshold performed best.

several of our analyses pointed towards similar numbers for an optimal k, and these optimal k values were reasonable, as they were close to the number of gold standard classes.


Given a full form F F F , its valid abbreviation A A A should have the character number constraints: 0 < |A A A| < |F F F |.

we can assume that a negative full form has an "invalid abbreviation" A A A with |A A A| = 0 1 http://ictclas.org/ or |A A A| = |F F F |.


Research in speech summarization -unlike its text-based counterpart -carries intrinsic difficulties, which draw their origins from the noisy nature of the data under consideration: imperfect ASR transcripts due to recognition errors, lack of proper segmentation, etc.

it also offers some advantages by making it possible to leverage extra-textual information such as emotion and other speaker states through an incorporation of prosodic knowledge into the summarization model.


Maskey and Hirschberg (2006) use an HMM to perform summarization by relying solely on prosodic features.

their model -unlike ours -is supervised.


In this scenario a metric such as ROUGE -which effectively measures n-gram overlap -would reward the "begin" summary for including key terms that appear several times in the gold standard summaries.

for longer summaries, where lexical variation is more pronounced, prosodic information provides a robust source of intelligence to select noteworthy utterances.


Their Chi-Square test gives weightage also to those documents in which the word is absent while calculating the score.

their idea of selecting hitting words, considers multiple occurrences of a word in a single doc-ument as one.


Several studies linked the personal lexicon with health and the health-related behavior of the individual.

few text mining studies were performed to analyze opinions expressed in a large volume of user-written Web content.


Surgeries related to HL are the most common surgeries in North America; thus, they affect many patients and their families.

there are only a few health forums dedicated to Hearing Loss (HL).


Verbs are the next common in the lexicon and give good indication of subjectivity.

as verbs are used in many different forms and have many meanings, just relying on the verb polarity will misguide the prediction in cases where the verbs are used in some other senses, e.g., he uses a car is neutral, when he was used has a negative sense.


Takamura et al., (2007) used the Potts model for extracting semantic orientation of phrases (pair of adjective and a noun): positive, negative or neutral.

to that, we have used the Potts model for identifying the emotional class (es) of a word.


The same issue goes for the development set.

even if we figured out a way to split the corpus into sets of the appropriate numbers of songs while taking into consideration that the artists for each set must be unique, there are further factors that could skew the results.


Group B has members that describe movement from one place to another, as does Group C. Groups D and E in Table 5 faithfully replicate the Levin Classes avoid-52, adn learn-14 from Table 1.

groups F and g seem to not form coherent semantic groups.


(Uszkoreit 2011) introduced a bootstrapping system for relation extraction rules, which achieved good performance under some circumstances.

most previous semi-supervised methods have large performance gaps from supervised systems, and their performance depends on the choice of seeds (Vyas et al., 2009;Kozareva and Hovy, 2010).


In this case, their settings favor instances with a positive prediction from the local classifier and a negative prediction from the global classifier, thus influencing the selection of queries.

in terms of diversity of queries, the global classifier is more capable of discovering unseen instances in the local feature space.


When the number of instances is large enough, the statistical model will effectively incorporate these entity type constraints as long as entity types are extracted as features.

in active learning, even with suitable training examples, we will select and present to the user some instances violating these constraints.


In this paper, we, similarly, present the state-of-the-art approach as a graph.

unlike Kok and Brockett (2010), we treat English phrases (instead of multilingual phrases) as nodes.


Although the weights of parameters carry the linguistic properties, the proposed factors could be considered separately for examining and comparing the individual effectiveness in our framework.

other factors could be taken in consideration.


Research has also focused on classifying polarities relative to contexts (Wilson et al., 2009).

only limited research has taken place on applying polarity classification techniques on complex domains such as the medical domain (Niu et al., 2005;Sarker et al., 2011).


The majority of previous work has primarily focused on clustering a static collection of short documents [Rangrej et al., 2011, Tsur et al., 2012, or on using surface features to compute pairwise similarity between microtext [Reuter et al., 2011, Li et al., 2012.

the challenge of how to effectively cluster microtext in dynamic data streams has not been well addressed.


Using this model, a tweet represents a data point in where d is the size of the word vocabulary and v j is the TF-IDF weight of j th word in tweet m i .

in a dynamic microtext stream, word vocabulary changes and the number of tweets increases over time, making it computationally expensive to recalibrate the inverse document frequency of TF-IDF.


Decaying Recursive Similarity: We considered neighborhood of an interpretation (hyperlinked entities, parent categories, subcategories, and grand parent pages) in the similarity measurement.

an appropriate weight which decays with distance is set to avoid influence of farther neighborhood nodes.


Mentioned language is a common form of metalanguage, used to perform the full variety of language tasks discussed in the introduction.

other metalinguistic constructions draw attention to tokens outside of the referring sentence.


A lack of phraselevel annotations in their corpus as well as substantial noise made it suboptimal for the present effort.

it is possible (if not likely) that indicators of metalanguage differ between written and spoken English, lending importance to the Anderson corpus as a resource.


This work is applicable to languages with concatenative morphology where suffixes stack one after another.

problems arise when there are phonemic changes in the boundaries of the stems and suffixes (sandhi).


Dependency parsing based methods have achieved much success in SRL.

due to errors in dependency parsing, there remains a large performance gap between SRL based on oracle parses and SRL based on automatic parses in practice.


The bulk of previous work on automatic SRL has primarily focused on using full constituent parse of sentences to define argument boundaries and to extract relevant information for training classifiers.

there have been some attempts at relaxing the necessity of using syntactic information derived from full parse trees.


Table 4 shows the Chinese SRL results after adding the N-best dependency parsing related features.

it is not surprising that SRL can get better performance when N > 1, because the larger N, a more accurate dependency parsing results can be  the improvement declines when N = 10.


This again works well for some entities, where Word-Net contains reasonably specific concepts (e.g., occupations and nationalities for people, industries for organizations) but not too well for specialized concepts in specific domains.

in our approach, WordNet is only used to provide the labels for very few relations (5, 000) that are used in training and (separately) in evaluation.


The DP is usually placed before hour, such as ling-chen san-dian (3:00am), wu-ye shier-dian (0:00).

the boundaries of different phases are not clear, such as xia-wu/wan-shang liu-dian (6:00 in the afternoon/evening) .


Chinese lunar time system uses a similar way to denote time as the Gregorian system.

it refers to the movement of the moon to count months.


While this improvement is modest, it suggests that features aside from the mathematical term itself can be helpful.

the system works well even without this feature.


The data generated can then be used to train a classifier that allows automatic sense-tagging of mathematical expressions.

to natural language text, mathematical expressions require specific processing methods.


For training and development datasets, anaphoric annotations were provided by the organizers.

for test set there was no annotation available.